Self Supervised Crop Type Classification using MultiSpectral Remote Sensing

Abstract

Accurate and efficient crop classification using multispectral remotely sensed data is essential for crop yield estimation and agricultural management. However, one of the major challenges in this task is the limited availability of labeled data, which hinders the ability to achieve good classification results. In our study, we propose a two-step approach to address this challenge and improve crop classification accuracy. Firstly, we employ a self-supervised pre-training step using data extracted from Sentinel Hub. This pre-training step utilizes unlabeled data to initialize the model and capture valuable information about crop growth patterns. By leveraging the abundant unlabeled data, the model learns to extract meaningful features and understand the contextual relationships within the data. This enhances the model’s ability to classify crops accurately. In the second step, we perform transfer learning for supervised classification using labeled data. The weights obtained from the pre-training step serve as the starting point, and the model is further optimized using the labeled data to improve its classification accuracy. Our experiments demonstrate that incorporating self-supervised pre-training leads to faster convergence and better results compared to training without pre-training. The pre-training phase enables the model to acquire prior knowledge about crop growth patterns, which facilitates more efficient learning and better generalization to unseen data during the supervised classification step. Moreover, by utilizing multispectral data instead of the traditional 4-channel data, our approach captures more comprehensive and discriminative information, further enhancing the classification performance.